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10 free GitHub repos that can save you hundreds every month. open-source. free to use. better than most people think. ↓ 1️⃣ OpenScreen — an alternative to Screen Studio ($29/mo) • record polished demos on macOS, Windows, and Linux • automatic cursor effects, blur, annotations, GIF + MP4 export...

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Kshitij Mishra | AI & Tech

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16,359 görüntüleme • 17 gün önce •via X (Twitter)

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most traders pay $3,000+/month for tools GitHub replaced for free. 9 repos. zero subscriptions. 1. OpenBB → replaces Bloomberg Terminal ($2,000/mo) financial data platform built for AI agents and quants connects natively to claude via MCP. most people don't know this. 2. freqtrade → replaces paid crypto bot services ($100/mo) ML strategy optimization. runs on binance, bybit, hyperliquid and 10+ others 34,000 stars. free and always will be. 3. hummingbot → replaces HFT bot platforms ($200/mo) $34B+ in user-generated trading volume has a native claude MCP integration. connect your AI directly to 140+ exchanges. 4. FinGPT → replaces financial AI subscriptions ($150/mo) open-source LLMs that outperform GPT-4 on market sentiment bloomberg spent $3M training theirs. this costs $17 to fine-tune. 5. NautilusTrader → replaces institutional trading platforms ($500/mo) production-grade. rust-native. fast enough to train RL trading agents same codebase for backtesting and live. zero rewrite needed. 6. QuantConnect Lean → replaces paid quant research platforms ($100/mo) professional algo trading engine. python + C# from backtest to live in one click. used by 200K+ quants worldwide. 7. jesse → replaces TradingView algo subscriptions ($25/mo) advanced crypto trading framework for serious strategy builders clean. powerful. no bloat. 8. vectorbt → replaces paid backtesting tools ($80/mo) fastest backtesting library in existence tests thousands of strategies in seconds. pandas-based. 9. FinRL → replaces custom AI trading infrastructure ($300/mo) financial reinforcement learning. train your own trading AI. from the same team behind FinGPT. 10. AlphaCartel Setup → replaces hedge fund signal services ($300/mo) this is where all 9 repos above connect into one working system claude-powered bots. live signals. no-code setup. community of traders already printing. total before: ~$3,455/month total now: $0 + alphacartel like + bookmark. you'll need this.
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most traders pay $3,000+/month for tools GitHub replaced for free. 9 repos. zero subscriptions. 1. OpenBB → replaces Bloomberg Terminal ($2,000/mo) financial data platform built for AI agents and quants connects natively to claude via MCP. most people don't know this. 2. freqtrade → replaces paid crypto bot services ($100/mo) ML strategy optimization. runs on binance, bybit, hyperliquid and 10+ others 34,000 stars. free and always will be. 3. hummingbot → replaces HFT bot platforms ($200/mo) $34B+ in user-generated trading volume has a native claude MCP integration. connect your AI directly to 140+ exchanges. 4. FinGPT → replaces financial AI subscriptions ($150/mo) open-source LLMs that outperform GPT-4 on market sentiment bloomberg spent $3M training theirs. this costs $17 to fine-tune. 5. NautilusTrader → replaces institutional trading platforms ($500/mo) production-grade. rust-native. fast enough to train RL trading agents same codebase for backtesting and live. zero rewrite needed. 6. QuantConnect Lean → replaces paid quant research platforms ($100/mo) professional algo trading engine. python + C# from backtest to live in one click. used by 200K+ quants worldwide. 7. jesse → replaces TradingView algo subscriptions ($25/mo) advanced crypto trading framework for serious strategy builders clean. powerful. no bloat. 8. vectorbt → replaces paid backtesting tools ($80/mo) fastest backtesting library in existence tests thousands of strategies in seconds. pandas-based. 9. FinRL → replaces custom AI trading infrastructure ($300/mo) financial reinforcement learning. train your own trading AI. from the same team behind FinGPT. 10. AlphaCartel Setup → replaces hedge fund signal services ($300/mo) this is where all 9 repos above connect into one working system claude-powered bots. live signals. no-code setup. community of traders already printing. total before: ~$3,455/month total now: $0 + alphacartel like + bookmark. you'll need this.

AI Bulls

106,909 görüntüleme • 3 ay önce

10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. — 1️⃣ NotebookLM — your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise — 2️⃣ Google AI Studio — the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits — 3️⃣ Gemini CLI — google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) — 4️⃣ Jules — async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5️⃣ Stitch — text → UI → code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work — 6️⃣ Gemma 4 — open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference — 7️⃣ Illuminate — papers → podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading — 8️⃣ Learn About (LearnLM) — adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics — 9️⃣ Google Labs FX (ImageFX + Flow + MusicFX) — free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music — those stay free. replaces: midjourney + suno (free tier only — runway-level video gen is paid) — 🔟 Google Colab — free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals — a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done — especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this 🔔
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10 free Google AI tools nobody talks about. while everyone's burning $20/mo on chatgpt and claude, google quietly shipped a stack worth $200+/mo. all free. all yours. — 1️⃣ NotebookLM — your second brain upload sources (PDFs, websites, audio, YouTube). it summarizes, builds mind maps, generates quizzes, drafts slide decks, even turns your notes into a podcast you can listen to on a walk. free tier: 100 notebooks, 50 sources each, 50 chats/day, 3 audio overviews/day. replaces: notion AI + perplexity + readwise — 2️⃣ Google AI Studio — the free gemini playground web playground for gemini 3 pro and flash with a free API key. generous limits. paste a 1M-token context window and watch it actually use it. faster than the openai playground and free where openai charges per token. replaces: openai playground + paid API credits — 3️⃣ Gemini CLI — google's open-source terminal agent apache 2.0 licensed. one command (npx @google/gemini-cli) and you've got an agent in your terminal that reads your codebase, runs shell commands, and ships PRs. drop-in claude code alternative. replaces: claude code ($20/mo by default) — 4️⃣ Jules — async coding agent assign jules a github issue. it spins up a cloud VM, clones your repo, writes the plan, makes the changes, opens a PR. free tier: 15 tasks/day, 3 concurrent, runs on gemini flash. replaces: devin ($20/mo+) + cursor agent 5️⃣ Stitch — text → UI → code google's free figma killer. describe an interface, get production-ready HTML/CSS/Tailwind + figma export. march 2026 update added voice canvas, infinite canvas, and MCP integration with cursor. 350 standard + 200 experimental generations/month free. replaces: galileo AI + early-stage figma work — 6️⃣ Gemma 4 — open-weight LLM google's flagship open model. apache 2.0. 2B, 4B, 26B-MoE, and 31B variants. 256K context. runs on ollama with one command. quantized versions run on a 4090 or beefy laptop. replaces: paying for hosted LLM inference — 7️⃣ Illuminate — papers → podcasts paste an arxiv preprint link. illuminate turns dense research papers into a 6-8 min conversation between two AI hosts breaking it down. perfect for commute reading you can't do at a desk. note: still in waitlist for some regions. replaces: snipd + manual research reading — 8️⃣ Learn About (LearnLM) — adaptive AI tutor drop in any topic you're stuck on. highlight a word, click "go deeper," and the interface adapts in real time to your comprehension level. visual explanations, follow-up questions, the works. replaces: paid tutoring on niche topics — 9️⃣ Google Labs FX (ImageFX + Flow + MusicFX) — free imagen, veo, musicLM google labs creative suite. text-to-image (imagen 4), text-to-video (veo via Flow), text-to-music (musicLM). free tier: limited daily generations. the heavy veo 3.1 features are paid (AI Pro $19.99/mo). still worth using for image and music — those stay free. replaces: midjourney + suno (free tier only — runway-level video gen is paid) — 🔟 Google Colab — free GPU notebooks free T4 GPU + 12GB RAM in a browser tab. enough to fine-tune small models, run stable diffusion, prototype agents. the launching pad for half the ML projects on github. replaces: paid cloud GPU rentals — a quick honest note: these tools aren't 1:1 better than the paid versions they replace. but they're decent enough to get most things done — especially if you're not a heavy user or you've got little funds to play with. i've put all 10 in a public github repo (link in comments). follow + turn on post notifications for more useful posts like this 🔔

m0h

11,673 görüntüleme • 2 ay önce

🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.
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🚨 Claude Code costs $200/month. GitHub Copilot costs $19/month. Jack Dorsey's company built a free alternative. 35,000 GitHub stars. It's called Goose. An open source AI agent built by Block that goes beyond code suggestions. It installs, executes, edits, and tests. With any LLM you choose. Not autocomplete. Not suggestions. A full autonomous agent that takes actions on your computer. No vendor lock-in. No monthly subscription. Bring your own model. Here's what Goose does: → Works with ANY LLM. Claude, GPT, Gemini, Llama, DeepSeek, Ollama. Your choice. → Reads and understands your entire codebase → Writes, edits, and refactors code across multiple files → Runs shell commands and installs dependencies → Executes and debugs your code automatically → Extensible through MCP. Connect it to any external tool. → Desktop app, CLI, and web interface. Pick your workflow. → Written in Rust. Fast. Lightweight. No bloat. Here's the wildest part: Block is a $40 billion company. They built Cash App, Square, and TIDAL. They use Goose internally. Then they open sourced the entire thing. This isn't a side project from a random developer. This is production-grade tooling from a company that processes billions in payments. Built for their own engineers. Given to everyone. Claude Code: $200/month. Locked to Claude. GitHub Copilot: $19/month. Locked to GitHub. Cursor: $20/month. Locked to their editor. Goose: Free. Any LLM. Any editor. Any workflow. Forever. 35.3K GitHub stars. 3.3K forks. 4,078 commits. Built by Block. 100% Open Source. Apache 2.0 License.

Nav Toor

392,982 görüntüleme • 3 ay önce

5 startup ideas you can build and resell using only ElevenLabs Agents each one costs $0.08/min to run and replaces $2-5k/mo in human labor Let's break them down ↓ 1. AI Receptionist for Local Businesses dentists, salons, clinics, they all pay $2-3k/mo for someone to answer phones build a voice agent that: - answers calls 24/7 - books appointments - handles FAQs - speaks the client's language who ALREADY uses it: ~31% of local service businesses who STILL needs it: ~69% (your market) white-label it, charge $300-500/mo per client your cost per client: ~$30/mo in minutes 2. Multilingual Customer Support ElevenLabs agents speak 70+ languages natively e-commerce brands selling internationally need support in 5-10 languages minimum one agent replaces a 5-person multilingual team who ALREADY uses it: ~36% of e-commerce businesses who STILL needs it: ~64% and most of them are mid-market brands scaling globally sell 24/7 coverage, mark up the minutes, charge per-seat 3. AI Sales Qualifier (SDR Replacement) voice agent calls inbound leads, asks 5-10 qualifying questions, books meetings directly into the sales team's calendar startups pay $4-6k/mo per SDR you charge $1.5k/mo for an agent that works 24/7 and never misses a lead who ALREADY uses it: ~27% of mid-market teams who STILL needs it: ~73% and 22% already fully replaced human SDRs plug it into any CRM like HubSpot, Salesforce, Pipedrive 4. Restaurant Order-Taking Agent phone ordering for restaurants, pizzerias, takeout spots the agent takes the order, upsells sides and drinks, confirms, pushes to the POS who ALREADY uses it: ~34% of restaurants who STILL needs it: ~66% (expected to hit 50%+ in major cities this year) build one integration template → sell to 100+ restaurants at $200/mo each that's $20k/mo from one vertical 5. Real Estate Showing Scheduler agents answer property inquiry calls, give listing details, qualify buyers, and book viewings (all mid-call) realtors spend hours on phone scheduling who ALREADY uses it: ~18% use voice AI specifically who STILL needs it: ~82% while 82% of agents already use some form of AI, almost none have voice agents charge per listing or flat monthly integrates with their calendar + CRM -------- How to build any of these: - sign up for ElevenLabs (startups get $4k free credits) - pick your niche - build the agent with their no-code platform - connect it to GPT or Claude for the brain - plug in scheduling/CRM via API - white-label it under your brand you don't need to build AI, you need to sell AI to people who don't know it exists yet reply "ELEVEN" + RT and i'll send you a free guide so you can build this too
0:43

5 startup ideas you can build and resell using only ElevenLabs Agents each one costs $0.08/min to run and replaces $2-5k/mo in human labor Let's break them down ↓ 1. AI Receptionist for Local Businesses dentists, salons, clinics, they all pay $2-3k/mo for someone to answer phones build a voice agent that: - answers calls 24/7 - books appointments - handles FAQs - speaks the client's language who ALREADY uses it: ~31% of local service businesses who STILL needs it: ~69% (your market) white-label it, charge $300-500/mo per client your cost per client: ~$30/mo in minutes 2. Multilingual Customer Support ElevenLabs agents speak 70+ languages natively e-commerce brands selling internationally need support in 5-10 languages minimum one agent replaces a 5-person multilingual team who ALREADY uses it: ~36% of e-commerce businesses who STILL needs it: ~64% and most of them are mid-market brands scaling globally sell 24/7 coverage, mark up the minutes, charge per-seat 3. AI Sales Qualifier (SDR Replacement) voice agent calls inbound leads, asks 5-10 qualifying questions, books meetings directly into the sales team's calendar startups pay $4-6k/mo per SDR you charge $1.5k/mo for an agent that works 24/7 and never misses a lead who ALREADY uses it: ~27% of mid-market teams who STILL needs it: ~73% and 22% already fully replaced human SDRs plug it into any CRM like HubSpot, Salesforce, Pipedrive 4. Restaurant Order-Taking Agent phone ordering for restaurants, pizzerias, takeout spots the agent takes the order, upsells sides and drinks, confirms, pushes to the POS who ALREADY uses it: ~34% of restaurants who STILL needs it: ~66% (expected to hit 50%+ in major cities this year) build one integration template → sell to 100+ restaurants at $200/mo each that's $20k/mo from one vertical 5. Real Estate Showing Scheduler agents answer property inquiry calls, give listing details, qualify buyers, and book viewings (all mid-call) realtors spend hours on phone scheduling who ALREADY uses it: ~18% use voice AI specifically who STILL needs it: ~82% while 82% of agents already use some form of AI, almost none have voice agents charge per listing or flat monthly integrates with their calendar + CRM -------- How to build any of these: - sign up for ElevenLabs (startups get $4k free credits) - pick your niche - build the agent with their no-code platform - connect it to GPT or Claude for the brain - plug in scheduling/CRM via API - white-label it under your brand you don't need to build AI, you need to sell AI to people who don't know it exists yet reply "ELEVEN" + RT and i'll send you a free guide so you can build this too

Ronin

774,425 görüntüleme • 2 ay önce

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.
0:47

google just released 15 AI tools that are completely FREE and can save thousands of $$$ every single monthly. all open-source. MIT licensed. save this in your bookmark." 1️⃣ pomelli ( builds your entire brand identity from just your website URL, then generates on-brand social posts, campaigns, and images. a free jasper + a junior brand marketer. no watermark, no gen cap in beta. 2️⃣ stitch ( describe an interface, get production-ready HTML/CSS/Tailwind + a figma export. google's free figma killer. 350 designs a month without paying a cent. 3️⃣ opal ( build no-code AI mini-apps and multi-step workflows just by describing them in plain english. basically a free n8n with Gemini baked in. no usage caps. 4️⃣ antigravity ( agentic IDE that plans, edits across files, and builds full apps from a single prompt. the "cursor-killer," free tier runs Gemini 3 Pro + Claude Sonnet 4.5. 5️⃣ mixboard ( canva x pinterest for AI. generate and remix images into moodboards, then edit right on the canvas with plain language. free while in beta. 6️⃣ disco ( turns your messy open browser tabs into custom interactive AI apps. competitor tabs become a comparison matrix, travel tabs become an itinerary. zero code. 7️⃣ notebookLM ( upload PDFs, videos, and notes, get instant summaries, mind maps, quizzes, even a podcast of your own material. replaces notion AI + perplexity + readwise. 8️⃣ Learn Your Way ( turns any topic into a personalized, AI-built course. immersive text, audio lessons, mind maps, and quizzes adapted to how you actually learn. free tutoring. 🔟 Google AI Studio ( prototype and ship AI apps in seconds with a free API key and a 1M-token context window. replaces the openai playground + paid API credits. 1️⃣1️⃣ Jules ( assign it a github issue, it spins up a VM, writes a plan, makes the changes, and opens a PR. a free devin. 15 tasks a day. 1️⃣2️⃣ Gemini CLI ( claude-code in your terminal. reads your codebase, runs commands, ships PRs. genuinely open source (Apache 2.0) and free. 1️⃣3️⃣ Code Wiki ( point it at any public github repo, get a living, self-updating wiki with architecture diagrams and a Gemini chat, every section hyperlinked to the code. 1️⃣4️⃣ Firebase Studio ( AI cockpit for your backend and cloud logic. heads up: existing users only, google is winding it down, so don't start a new project here. 1️⃣5️⃣ Gemini Code Assist ( free github copilot: 180k code completions a month + AI code reviews in VS Code, JetBrains, and github. the free tier that actually out-specs copilot. Follow me and turn on 🔔 post notifications.

m0h

77,102 görüntüleme • 4 gün önce

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!
0:25

Big moment for Postgres! AI coding tools have been surprisingly bad at writing Postgres code. Not because the models are dumb, but because of how they learned SQL in the first place. LLMs are trained on the internet, which is full of outdated Stack Overflow answers and quick-fix tutorials. So when you ask an AI to generate a schema, it gives you something that technically runs but misses decades of Postgres evolution, like: - No GENERATED ALWAYS AS IDENTITY (added in PG10) - No expression or partial indexes - No NULLS NOT DISTINCT (PG15) - Missing CHECK constraints and proper foreign keys - Generic naming that tells you nothing But this is actually a solvable problem. You can teach AI tools to write better Postgres by giving them access to the right documentation at inference time. This exact solution is actually implemented in the newly released pg-aiguide by Tiger Data - Creators of TimescaleDB, which is an open-source MCP server that provides coding tools access to 35 years of Postgres expertise. In a gist, the MCP server enables: - Semantic search over the official PostgreSQL manual (version-aware, so it knows PG14 vs PG17 differences) - Curated skills with opinionated best practices for schema design, indexing, and constraints. I ran an experiment with Claude Code to see how well this works, and worked with the team to put this together. Prompt: "Generate a schema for an e-commerce site twice, one with the MCP server disabled, one with it enabled. Finally, run an assessment to compare the generated schemas." The run with the MCP server led to: - 420% more indexes (including partial and expression indexes) - 235% more constraints - 60% more tables (proper normalization) - 11 automation functions and triggers - Modern PG17 patterns throughout The MCP-assisted schema had proper data integrity, performance optimizations baked in, and followed naming conventions that actually make sense in production. pg-aiguide works with Claude Code, Cursor, VS Code, and any MCP-compatible tool. It's free and fully open source. I have shared the repo in the replies!

Avi Chawla

186,931 görüntüleme • 7 ay önce

Most traders spend thousands of dollars on tools. Meanwhile, free GitHub repos can replace almost everything - at zero cost. Bookmark this, so you don't lose it. 1. FinceptTerminal (+10.7K ★) • A real Bloomberg Terminal alternative - built in C++20 + Qt6. • 37 AI agents modeled after Buffett, Munger, and Graham. 🔗 2. TradingAgents (+1.5K ★) • Multi-agent trading system (UCLA/MIT research). • Fundamental + sentiment + technical + risk agents • Works with Claude, GPT, Gemini, Grok 🔗 3. last30days-skill (+1.4K ★) • AI agent skill for recent signal (last 30 days) across Reddit, X, YouTube, HN, Polymarket. 🔗 4. daily_stock_analysis (+31K ★) • LLM-powered stock analysis engine. • US + A-share + H-share markets • Daily dashboards with entry/exit levels • Auto delivery via Telegram, Discord, Email 🔗 5. QuantDinger (+919 ★) • Self-hosted AI quant OS. • Strategy generation + backtesting + live trading • Crypto, stocks (IBKR), forex (MT5) 🔗 6. HKUDS/Vibe-Trading (+611 ★) • Natural language → strategy → backtest → execution. • 70+ finance skills • Export to TradingView / MT5 🔗 7. freqtrade (+467 ★) • Open-source crypto trading bot. • Multi-exchange support • Backtesting + optimization • Telegram control 🔗 8. OpenBB (+447 ★) • Open-source Bloomberg Terminal alternative. • Stocks, crypto, options, macro • AI-native integrations (MCP) 🔗 9. 500 AI Agents Projects (+386 ★) • Curated collection of real-world AI agent use cases (including finance). 🔗 10. AlphaCartel Discord (+1280 ★) • 100% free community for AI traders:
0:37

Most traders spend thousands of dollars on tools. Meanwhile, free GitHub repos can replace almost everything - at zero cost. Bookmark this, so you don't lose it. 1. FinceptTerminal (+10.7K ★) • A real Bloomberg Terminal alternative - built in C++20 + Qt6. • 37 AI agents modeled after Buffett, Munger, and Graham. 🔗 2. TradingAgents (+1.5K ★) • Multi-agent trading system (UCLA/MIT research). • Fundamental + sentiment + technical + risk agents • Works with Claude, GPT, Gemini, Grok 🔗 3. last30days-skill (+1.4K ★) • AI agent skill for recent signal (last 30 days) across Reddit, X, YouTube, HN, Polymarket. 🔗 4. daily_stock_analysis (+31K ★) • LLM-powered stock analysis engine. • US + A-share + H-share markets • Daily dashboards with entry/exit levels • Auto delivery via Telegram, Discord, Email 🔗 5. QuantDinger (+919 ★) • Self-hosted AI quant OS. • Strategy generation + backtesting + live trading • Crypto, stocks (IBKR), forex (MT5) 🔗 6. HKUDS/Vibe-Trading (+611 ★) • Natural language → strategy → backtest → execution. • 70+ finance skills • Export to TradingView / MT5 🔗 7. freqtrade (+467 ★) • Open-source crypto trading bot. • Multi-exchange support • Backtesting + optimization • Telegram control 🔗 8. OpenBB (+447 ★) • Open-source Bloomberg Terminal alternative. • Stocks, crypto, options, macro • AI-native integrations (MCP) 🔗 9. 500 AI Agents Projects (+386 ★) • Curated collection of real-world AI agent use cases (including finance). 🔗 10. AlphaCartel Discord (+1280 ★) • 100% free community for AI traders:

AlphaCartel

30,801 görüntüleme • 3 ay önce

-> someone cloned claude -> design interface and -> made it completely free -> it's work on YouTube -> and also suitable for kids -> it’s called open design -> and it’s live on github -> same clean split-screen ui -> you get in claude artifacts -> prompt on the left, live -> design/code preview on -> the right, type what you -> want to build and it -> generates the ui in real -> time, but here’s the twist -> you pick the ai model -> not locked into one -> company, want to use -> gemini, mistral, llama, -> deepseek any model -> with an api work -> if you’re running local -> models with ollama -> that works too -> no subscription walls -> the big difference -> vs claude artifacts -> works with any free -> ai model you’re not -> paying $20/mo just to -> design, use free tiers -> local models, or whatever -> you already have access to -> fully local, your prompts -> and code never leave -> your machine unless -> you want them to -> no data training -> no cloud storage -> privacy by default -> no usage limits -> claude cuts you off -> after a few designs -> here you can generate, -> iterate, break things -> and rebuild all day -> the only limit is your -> don’t like how a button -> works, change it -> want to add your own -> components, go ahead -> you own the tool -> so if you’ve been gatekept -> by paywalls or worried -> about sensitive prompts -> going to some company’s -> servers, this fixes that. -> same workflow, more -> control, zero monthly fee
0:20

-> someone cloned claude -> design interface and -> made it completely free -> it's work on YouTube -> and also suitable for kids -> it’s called open design -> and it’s live on github -> same clean split-screen ui -> you get in claude artifacts -> prompt on the left, live -> design/code preview on -> the right, type what you -> want to build and it -> generates the ui in real -> time, but here’s the twist -> you pick the ai model -> not locked into one -> company, want to use -> gemini, mistral, llama, -> deepseek any model -> with an api work -> if you’re running local -> models with ollama -> that works too -> no subscription walls -> the big difference -> vs claude artifacts -> works with any free -> ai model you’re not -> paying $20/mo just to -> design, use free tiers -> local models, or whatever -> you already have access to -> fully local, your prompts -> and code never leave -> your machine unless -> you want them to -> no data training -> no cloud storage -> privacy by default -> no usage limits -> claude cuts you off -> after a few designs -> here you can generate, -> iterate, break things -> and rebuild all day -> the only limit is your -> don’t like how a button -> works, change it -> want to add your own -> components, go ahead -> you own the tool -> so if you’ve been gatekept -> by paywalls or worried -> about sensitive prompts -> going to some company’s -> servers, this fixes that. -> same workflow, more -> control, zero monthly fee

BeingInvested

12,134 görüntüleme • 1 ay önce

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch
27:27

how to use firecrawl to give your AI eyes and actually build startups that outperform 99% of apps: 1. your AI is smart but blind. it can't go to a website, read a page, or grab data on its own. firecrawl fixes that. you put in a URL. you get back clean markdown, structured JSON, screenshots. feed it to any model. 2. three lines of code. that's it. no proxies. no anti-bot detection. no custom scrapers that break when a site changes. one API call. clean data back in seconds. works on 98%+ of sites. 3. firecrawl has six core capabilities: scrape a single page. crawl an entire site. map all URLs on a domain. search google and return full content. an agent endpoint where you describe what you want and it goes and finds it. and a browser sandbox where AI controls a real browser like filling forms, clicking buttons, handles logins. 4. the agent endpoint is wild. you can say "find all of YC's winter 24 dev tool companies and their founders and emails" and get back structured data. or "compare pricing tiers across stripe, square, and paypal" and get a side-by-side table. 5. the browser sandbox lets your AI stay logged in across sessions, navigate pagination, watch live as it browses. this is computer use without building the infrastructure yourself. 6. think of it in layers. every builder needs: an agent harness (claude code, cursor, codex), a search layer (perplexity, exa), a web data layer (firecrawl), an ops brain (obsidian, notion), and an outbound stack. the web data layer is the one most people are sleeping on. 7. this is the AWS moment for web data. in 2006 building a web app meant buying servers and managing racks. AWS said one API call, use our servers. some of the biggest companies of the last decade were built on that. firecrawl is doing the same thing for web data in 2026. 8. the framework i'd use for coming up with startup ideas building with clean data: take a massive horizontal platform. rebuild it for one niche using firecrawl. the vertical version always wins because people want specific, not generic. price for outcome. 9. a year ago firecrawl posted a job listing that said "please only apply if you're an AI agent." content creator agents. customer support agents. junior dev agents. it looked weird. it was a signal for where this is all going. the people who understand how to get clean web data, wrap it around an LLM, and package it as a product are the the ones with a 12-month head start. i use Firecrawl with Idea Browser . once you see what's possible with structured web data, you can't unsee it. episode is live on The Startup Ideas Podcast (SIP) 🧃 (full breakdown there) i tried to explain this as clear as possible for even the non technical. send it to a builder friend. watch

GREG ISENBERG

135,000 görüntüleme • 4 ay önce

BREAKING: Introducing All Access from Every 📧, our new membership tier for the best builders in AI All Access subs get the Builder Pack which includes $7,000 in credits and free usage to the models + tool stack we use Every 📧. All Access subscribers get: - $1,000 in Codex / @ChatGPTapp for Work credits - 12 months free of Cursor Pro+ - $4,000 in PostHog credits including self-driving to automatically fix bugs and identify issues in your production app - 1 year free of Framer - 6 months free of Notion And much more! (Did I mention $1,000 in Codex credits? It's time to build!) Get all access: Why All Access and the Builder Pack This is the best time in history to build something. For a long time, it’s been possible to one-shot impressive demos, but they’d fall flat the minute they hit production. But the release of GPT-5.6-Sol and Fable 5 heralds a new era: Everyone can build, launch, and maintain the software that they’ve always dreamed of. Everyone is a builder now. There’s just one catch: Building with AI is very expensive. (Ask me how I know.) (Alright, I’ll tell you. I accidentally used 2 billion tokens overnight this week on a big GPT-5.6-Sol run. Worth it.) This is unique in the history of technology. For most of the personal computing era, a billionaire and a solo builder could buy essentially the same top-of-the-line Mac. AI changes that: The more tokens you can afford, the more you can make. And we want to make that accessible to more people. That’s why the main feature of our new All Access plan is the Builder Pack: more than $7,000 in credits and discounts on the full stack we use to run Every, from idea to production—Codex, Claude, PostHog, Render, Gemini, FLORA, and more. Early-bird membership is only $500/year for the next 24 hours—and the Codex credits alone are worth $1,000. (I could’ve used it for my overnight run this week.) Now we’re handing it to you. Get all access: Meet the Builder Pack It's got more than $7,000 in offers from 10 of the AI products we use to write, design, build, and run Every 📧: BUILD - $1,000 in Codex credits plus one month of ChatGPT for business - Twelve months free of Cursor Pro+ - One month free of Claude Max - Three months free of Google AI Pro DESIGN - One year free of Framer Pro - One month free of FLORA © Max HOST - $300 in Render credits IMPROVE - $4,000 in PostHog credits - Six months free of Notion Business - Six months free of AgentMail (YC S25) We rely on these every day, and we tried to put together a package that helps you comprehensively for each part of the process of building and running software in AI. What comes with All Access - Everything in an existing paid Every membership: our daily writing, guides, camps, and software like Monologue, Cora, Sparkle, and Spiral - The Builder Pack, with more than $7,000 in partner offers - Unlimited email accounts use of Cora and unlimited Spiral usage - Members-only programming with me and the Every team and me Get All Access:
2:14

BREAKING: Introducing All Access from Every 📧, our new membership tier for the best builders in AI All Access subs get the Builder Pack which includes $7,000 in credits and free usage to the models + tool stack we use Every 📧. All Access subscribers get: - $1,000 in Codex / @ChatGPTapp for Work credits - 12 months free of Cursor Pro+ - $4,000 in PostHog credits including self-driving to automatically fix bugs and identify issues in your production app - 1 year free of Framer - 6 months free of Notion And much more! (Did I mention $1,000 in Codex credits? It's time to build!) Get all access: Why All Access and the Builder Pack This is the best time in history to build something. For a long time, it’s been possible to one-shot impressive demos, but they’d fall flat the minute they hit production. But the release of GPT-5.6-Sol and Fable 5 heralds a new era: Everyone can build, launch, and maintain the software that they’ve always dreamed of. Everyone is a builder now. There’s just one catch: Building with AI is very expensive. (Ask me how I know.) (Alright, I’ll tell you. I accidentally used 2 billion tokens overnight this week on a big GPT-5.6-Sol run. Worth it.) This is unique in the history of technology. For most of the personal computing era, a billionaire and a solo builder could buy essentially the same top-of-the-line Mac. AI changes that: The more tokens you can afford, the more you can make. And we want to make that accessible to more people. That’s why the main feature of our new All Access plan is the Builder Pack: more than $7,000 in credits and discounts on the full stack we use to run Every, from idea to production—Codex, Claude, PostHog, Render, Gemini, FLORA, and more. Early-bird membership is only $500/year for the next 24 hours—and the Codex credits alone are worth $1,000. (I could’ve used it for my overnight run this week.) Now we’re handing it to you. Get all access: Meet the Builder Pack It's got more than $7,000 in offers from 10 of the AI products we use to write, design, build, and run Every 📧: BUILD - $1,000 in Codex credits plus one month of ChatGPT for business - Twelve months free of Cursor Pro+ - One month free of Claude Max - Three months free of Google AI Pro DESIGN - One year free of Framer Pro - One month free of FLORA © Max HOST - $300 in Render credits IMPROVE - $4,000 in PostHog credits - Six months free of Notion Business - Six months free of AgentMail (YC S25) We rely on these every day, and we tried to put together a package that helps you comprehensively for each part of the process of building and running software in AI. What comes with All Access - Everything in an existing paid Every membership: our daily writing, guides, camps, and software like Monologue, Cora, Sparkle, and Spiral - The Builder Pack, with more than $7,000 in partner offers - Unlimited email accounts use of Cora and unlimited Spiral usage - Members-only programming with me and the Every team and me Get All Access:

Dan Shipper 📧

181,802 görüntüleme • 16 gün önce

🚨 JUST IN: MICROSOFT just open sourced a VOICE AI THAT TRANSCRIBES 60 MINUTES OF AUDIO in a single pass. 100% FREE. It knows who spoke. It knows when they spoke. It knows exactly what they said. All in one shot. No chunking. No context loss. It's called VibeVoice. Not a transcription tool. Not a basic speech to text wrapper. A frontier voice AI family with ASR, TTS, and real time streaming. All open source. All free. Here's what it actually does 👇 VibeVoice ASR - Speech Recognition: → Processes 60 minutes of continuous audio in a single pass → Never slices audio into chunks so global context is never lost → Identifies WHO spoke, WHEN they spoke and WHAT they said simultaneously → Supports customized hotwords for domain specific accuracy → Works in 50+ languages natively → Already adopted by Hugging Face Transformers library → Already being built on by the open source community BY PEOPLE WHO HAD NO IDEA THIS LEVEL OF ACCURACY WAS ALREADY FREE. VibeVoice TTS - Text to Speech: → Generates up to 90 minutes of speech in a single pass → Supports up to 4 distinct speakers in one conversation → Natural turn taking and speaker consistency throughout → Expressive speech that captures emotional nuances → Supports English, Chinese and multiple other languages VibeVoice Realtime - Streaming TTS: → Only 300 millisecond first audible latency → Streams text input in real time → 0.5B parameters so it actually deploys anywhere → Robust long form generation up to 10 minutes → Lightweight enough for production use today The core innovation nobody is talking about: Most voice AI models slice long audio into short chunks. Every time they slice, they lose context. Speaker tracking breaks. Semantic coherence breaks. Accuracy drops. VibeVoice uses continuous speech tokenizers running at an ultra low frame rate of 7.5 Hz. This preserves audio fidelity while dramatically boosting computational efficiency. The entire 60 minutes stays in context. Nothing gets lost. Nobody gets misidentified. The numbers: → VibeVoice ASR 7B - available now on Hugging Face → VibeVoice Realtime 0.5B - try it on Colab right now → 50+ supported languages → 11 distinct English voice styles → 9 multilingual speaker voices → Already integrated into Hugging Face Transformers → Finetuning code now available The wildest part? A voice powered input method called Vibing just built itself on top of VibeVoice ASR. Available on macOS and Windows right now. The open source community is already shipping products on top of this. 100% Open Source. Free to use. Free to fine tune. Free to build on. 🔖 Save this before your competitors find it first. 👇
0:44

🚨 JUST IN: MICROSOFT just open sourced a VOICE AI THAT TRANSCRIBES 60 MINUTES OF AUDIO in a single pass. 100% FREE. It knows who spoke. It knows when they spoke. It knows exactly what they said. All in one shot. No chunking. No context loss. It's called VibeVoice. Not a transcription tool. Not a basic speech to text wrapper. A frontier voice AI family with ASR, TTS, and real time streaming. All open source. All free. Here's what it actually does 👇 VibeVoice ASR - Speech Recognition: → Processes 60 minutes of continuous audio in a single pass → Never slices audio into chunks so global context is never lost → Identifies WHO spoke, WHEN they spoke and WHAT they said simultaneously → Supports customized hotwords for domain specific accuracy → Works in 50+ languages natively → Already adopted by Hugging Face Transformers library → Already being built on by the open source community BY PEOPLE WHO HAD NO IDEA THIS LEVEL OF ACCURACY WAS ALREADY FREE. VibeVoice TTS - Text to Speech: → Generates up to 90 minutes of speech in a single pass → Supports up to 4 distinct speakers in one conversation → Natural turn taking and speaker consistency throughout → Expressive speech that captures emotional nuances → Supports English, Chinese and multiple other languages VibeVoice Realtime - Streaming TTS: → Only 300 millisecond first audible latency → Streams text input in real time → 0.5B parameters so it actually deploys anywhere → Robust long form generation up to 10 minutes → Lightweight enough for production use today The core innovation nobody is talking about: Most voice AI models slice long audio into short chunks. Every time they slice, they lose context. Speaker tracking breaks. Semantic coherence breaks. Accuracy drops. VibeVoice uses continuous speech tokenizers running at an ultra low frame rate of 7.5 Hz. This preserves audio fidelity while dramatically boosting computational efficiency. The entire 60 minutes stays in context. Nothing gets lost. Nobody gets misidentified. The numbers: → VibeVoice ASR 7B - available now on Hugging Face → VibeVoice Realtime 0.5B - try it on Colab right now → 50+ supported languages → 11 distinct English voice styles → 9 multilingual speaker voices → Already integrated into Hugging Face Transformers → Finetuning code now available The wildest part? A voice powered input method called Vibing just built itself on top of VibeVoice ASR. Available on macOS and Windows right now. The open source community is already shipping products on top of this. 100% Open Source. Free to use. Free to fine tune. Free to build on. 🔖 Save this before your competitors find it first. 👇

Kanika

220,977 görüntüleme • 3 ay önce

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.
0:33

This Chinese guy created agents in Claude Code for MCP servers and single-handedly serves 6 marketing agencies a month from one iPhone, earning $5,000 from each. Inside he runs a pipeline of 7 agents on Claude Sonnet 4.6 that every Monday pulls a scan of the tech stack from a selected agency, develops an MCP server for its ad accounts, and over the course of a week brings it to production code ready to connect to Claude Desktop. No DevOps, no senior developer, no project manager. Just a Mac Mini in a work corner, an iPhone in the pocket, and a single API key. And traditional dev shops keep 5 people on project rates for the same contract, while his entire P&L is tokens, dirt-cheap hosting on Cloudflare, and Calendly. 7 agents run under a shared orchestrator-router and burn about 5 million tokens a day, which in the API bill comes out to $540 a month. The Mac Mini itself sits at home and keeps the entire orchestrator running 24/7, and from the iPhone the owner connects to it through a secure remote terminal and sees the output of any session right on the smartphone screen, wherever he happens to be. His starting system prompt looks like this: "you run a solo shop for custom MCP servers for marketing agencies. you hand out read-only tasks to 6 sub-agents and own all commits and shipping yourself. sub-agents: // Hunter (finds marketing agencies of 15 to 60 people that have no MCP access to Google Ads, Meta Ads, TikTok Ads, and HubSpot) // Mapper (pulls their tech stack, identifies 3 to 5 integration pains, and simultaneously writes the technical spec for the server: which tools, resources, and prompts to export through MCP, which auth flow and rate limit) // Coder (generates an MCP server in Python through the MCP SDK, deploys 8 to 15 tools for ad accounts and CRM) // Validator (connects the server to Claude Desktop, runs real client API keys in a sandbox, and checks for compliance with the MCP spec) // Shipper (writes a README, integration guide, deployment manual, packages the server, and hosts it on Cloudflare Workers or pushes to the GitHub of the client) // Mobile (always online on the iPhone, books demo calls in Calendly, picks up hot fixes, and confirms contracts through a secure remote terminal to the Mac Mini). only 1 owner agent works on 1 contract, no overlaps. you pull the owner out of observation mode only when a deal goes above $7,500 or the test coverage of the server drops below 85%." This prompt gives the system an understanding of its role and the limits of intervention from the very first line. It knows it is supposed to find agencies on its own. It knows it is supposed to bring every MCP server to production on its own. It knows it connects the live owner only on large deals or when the tests do not converge. → The pipeline runs without breaks, day or night → Hunter goes through about 130 marketing agencies on LinkedIn and Clutch per day → Mapper rolls out 4 audit reports with the tech stack and a final spec for each → Coder writes 1 to 2 MCP servers per week in Python with 8 to 15 tools → Validator validates every server through Claude Desktop with real client API keys → Shipper rolls out the full documentation package and pushes the finished product to Cloudflare Workers or the GitHub of the client And only when a contract breaks $7,500 or test coverage drops below 85% does the orchestrator pull the owner from whatever he is doing. And when the owner at that moment is behind the wheel or at a meeting in a coworking space, the Mobile agent in his iPhone picks up 1 contract in progress: confirms a meeting with the agency CMO in Calendly, opens a live demo of the MCP server through a secure terminal to the Mac Mini, and writes the test result to the shared state. The owner just swipes "approve" and in 15 minutes joins the Zoom demo. The fresh system log from last Wednesday looks like this: "hunter report: 132 agencies checked on LinkedIn and Clutch, 19 without MCP integrations, 8 with active requests for AI tooling in job posts, 4 with an open Q4 budget. passing to mapper." "coder: MCP server for Northwave Performance Marketing built in Python, 11 tools for Google Ads, Meta Ads, and GA4, 320 lines of code. exported to /Users/dev/mcp-shop/clients/northwave/server.py. validator connecting to Claude Desktop." "validator: 11 tools passed validation through Claude Desktop, test coverage 92%, average latency 380 ms. passing to shipper." "eval flag: contract with Pacific Reach Agency at $8,200 exceeds the approved limit of $7,500. sending for manual review." In his work setup there is no cloud server, no external team, and not even a separate office. At home sits a Mac Mini with a sandbox at /Users/dev/mcp-shop, on top runs an MCP router with a single API key to Claude, and the same key is forwarded to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo shop for custom MCP servers for marketing agencies: $540 a month on the API, about $30,000 into the account, and between them 7 system prompts, 1 Mac Mini in a work corner, and 1 iPhone that never leaves the pocket.

Blaze

55,926 görüntüleme • 2 ay önce

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10
51:39

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10

Dan Shipper 📧

15,645 görüntüleme • 10 ay önce

Perplexity declared war on the biggest open source AI movement of 2026. This changes how millions of people will interact with AI agents forever. Here is what happened and why almost nobody is talking about the real implications. OpenClaw exploded in January and it became one of the fastest growing open source projects in GitHub history.​ The premise was radical. An AI agent that runs on your own machine, connects to your messaging apps and actually does things while you sleep.​ Developers went wild and over 700 community built skills appeared on ClawHub. People were negotiating car deals, filing legal rebuttals, and building entire social networks run by AI agents.​ Then Perplexity showed up with something different. A cloud powered system that coordinates 20 frontier AI models at once.​ They call it Perplexity Computer and this week they went even further.​ They announced Personal Computer. An always on AI agent that lives on a Mac mini in your home, connected to your local files and Perplexity's secure servers around the clock.​ It never sleeps or stops working and you control it from any device, anywhere.​ But the real story is what CEO Aravind Srinivas said during the Q&A session at their inaugural developer conference in San Francisco.​ He called Perplexity Computer a product "meant for serious people."​ He talked about Uber drivers asking him when they could stop driving and let AI make them passive income. That, he said, is the actual vision and then he went directly after OpenClaw. He said even a former Perplexity engineer struggled to get OpenClaw running on their own machine.​ He warned about unvetted malware being imported through OpenClaw's community skill hub, with no control over what people are contributing.​ He called the hobbyist approach of managing 700 API keys and sub agent configuration files a dead end for mainstream adoption.​ And four years of building world class orchestration gives Perplexity something an open source project cannot match. Enterprise grade security for solopreneurs and businesses alike.​ On one side, OpenClaw represents radical openness. Your data stays local, you choose your own models, you own everything and the community builds the tools. On the other hand, Perplexity is betting that most people don’t want to be system administrators. They want results, security guarantees, and something that just works out of the box. The Personal Computer runs on Perplexity's SOC 2 certified infrastructure. Every sensitive action requires user approval, every action is logged, and there is a kill switch. The enterprise version connects natively to Snowflake, Salesforce, HubSpot, and hundreds of other platforms. Teams can query data warehouses and build financial models without waiting on an analytics team.​ The real question is not which product is technically better. The real question is whether the future of AI agents looks like Linux or looks like the iPhone. Because the Uber driver Srinivas described is not going to configure sub agent routing tables. That person needs something that works the moment they open it. And if Perplexity captures that market, the open source movement becomes a niche for developers instead of a revolution for everyone. That is the billion dollar bet being made right now.
2:02

Perplexity declared war on the biggest open source AI movement of 2026. This changes how millions of people will interact with AI agents forever. Here is what happened and why almost nobody is talking about the real implications. OpenClaw exploded in January and it became one of the fastest growing open source projects in GitHub history.​ The premise was radical. An AI agent that runs on your own machine, connects to your messaging apps and actually does things while you sleep.​ Developers went wild and over 700 community built skills appeared on ClawHub. People were negotiating car deals, filing legal rebuttals, and building entire social networks run by AI agents.​ Then Perplexity showed up with something different. A cloud powered system that coordinates 20 frontier AI models at once.​ They call it Perplexity Computer and this week they went even further.​ They announced Personal Computer. An always on AI agent that lives on a Mac mini in your home, connected to your local files and Perplexity's secure servers around the clock.​ It never sleeps or stops working and you control it from any device, anywhere.​ But the real story is what CEO Aravind Srinivas said during the Q&A session at their inaugural developer conference in San Francisco.​ He called Perplexity Computer a product "meant for serious people."​ He talked about Uber drivers asking him when they could stop driving and let AI make them passive income. That, he said, is the actual vision and then he went directly after OpenClaw. He said even a former Perplexity engineer struggled to get OpenClaw running on their own machine.​ He warned about unvetted malware being imported through OpenClaw's community skill hub, with no control over what people are contributing.​ He called the hobbyist approach of managing 700 API keys and sub agent configuration files a dead end for mainstream adoption.​ And four years of building world class orchestration gives Perplexity something an open source project cannot match. Enterprise grade security for solopreneurs and businesses alike.​ On one side, OpenClaw represents radical openness. Your data stays local, you choose your own models, you own everything and the community builds the tools. On the other hand, Perplexity is betting that most people don’t want to be system administrators. They want results, security guarantees, and something that just works out of the box. The Personal Computer runs on Perplexity's SOC 2 certified infrastructure. Every sensitive action requires user approval, every action is logged, and there is a kill switch. The enterprise version connects natively to Snowflake, Salesforce, HubSpot, and hundreds of other platforms. Teams can query data warehouses and build financial models without waiting on an analytics team.​ The real question is not which product is technically better. The real question is whether the future of AI agents looks like Linux or looks like the iPhone. Because the Uber driver Srinivas described is not going to configure sub agent routing tables. That person needs something that works the moment they open it. And if Perplexity captures that market, the open source movement becomes a niche for developers instead of a revolution for everyone. That is the billion dollar bet being made right now.

Milk Road AI

22,256 görüntüleme • 4 ay önce

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.
30:12

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

228,185 görüntüleme • 3 ay önce

CLAUDE "SKILLS" BUILT ME AN AI AGENT ARMY in 33 minutes Claude Skills fix one of the biggest problems in AI.... context rot. when you overload a model with long prompts, scattered files, and inconsistent instructions, it BREAKS. Claude Skills solve that by packaging everything an AI needs into one structured workflow. each Claude Skill includes: 1. instructions that define the role and process 2. reference materials that capture brand voice, examples, and style 3. code scripts that handle data and make outputs deterministic together, they act like a trained employee...reliable, consistent, and specialized. instead of dumping chaos into a chatbot, you’re building a clear operating system for how work gets done. in this The Startup Ideas Podcast (SIP) 🧃 episode, we built an entire team of AI employees inside Claude using Claude Skills. each had a specific job and worked in sync: – one generated UTM links and tracked campaign performance automatically – one ran A/B test idea generation using real website data and the ICE framework – one analyzed marketing reports, pulling actual numbers from CSVs and surfacing insights – one turned viral tweets into newsletters that matched my tone and writing style perfectly we then layered in sub-agents basically smaller Claude instances that split large tasks into specialized roles and passed results back. the result is a new kind of reliability. you can automate marketing, content creation, analytics, and reporting without losing accuracy, consistency, or creativity. Claude Skills turn messy, repeatable work into clean, reusable systems that anyone can deploy. in this episode, we walk through the full build writing the instructions, linking sub-agents, connecting data, and watching them collaborate in real time. thanks to amirmxt for dropping this sauce. most people charge thousands to teach this kind of AI systems design. this one’s free. watch the full masterclass on The Startup Ideas Podcast (SIP) 🧃 below on X or YouTube ( . claude skills isn't perfect but it's a step in the right direction pretty cool many will bookmark this but never try it but the key is getting your hands dirty learn and lightbulb moments will hit IM ROOTING FOR YOU
33:05

CLAUDE "SKILLS" BUILT ME AN AI AGENT ARMY in 33 minutes Claude Skills fix one of the biggest problems in AI.... context rot. when you overload a model with long prompts, scattered files, and inconsistent instructions, it BREAKS. Claude Skills solve that by packaging everything an AI needs into one structured workflow. each Claude Skill includes: 1. instructions that define the role and process 2. reference materials that capture brand voice, examples, and style 3. code scripts that handle data and make outputs deterministic together, they act like a trained employee...reliable, consistent, and specialized. instead of dumping chaos into a chatbot, you’re building a clear operating system for how work gets done. in this The Startup Ideas Podcast (SIP) 🧃 episode, we built an entire team of AI employees inside Claude using Claude Skills. each had a specific job and worked in sync: – one generated UTM links and tracked campaign performance automatically – one ran A/B test idea generation using real website data and the ICE framework – one analyzed marketing reports, pulling actual numbers from CSVs and surfacing insights – one turned viral tweets into newsletters that matched my tone and writing style perfectly we then layered in sub-agents basically smaller Claude instances that split large tasks into specialized roles and passed results back. the result is a new kind of reliability. you can automate marketing, content creation, analytics, and reporting without losing accuracy, consistency, or creativity. Claude Skills turn messy, repeatable work into clean, reusable systems that anyone can deploy. in this episode, we walk through the full build writing the instructions, linking sub-agents, connecting data, and watching them collaborate in real time. thanks to amirmxt for dropping this sauce. most people charge thousands to teach this kind of AI systems design. this one’s free. watch the full masterclass on The Startup Ideas Podcast (SIP) 🧃 below on X or YouTube ( . claude skills isn't perfect but it's a step in the right direction pretty cool many will bookmark this but never try it but the key is getting your hands dirty learn and lightbulb moments will hit IM ROOTING FOR YOU

GREG ISENBERG

189,753 görüntüleme • 9 ay önce

🚨BREAKING: An open-source agentic video production system just dropped. 11 pipelines, 49 tools, and a full product ad produced for $0.69 total. It's called OpenMontage. And it's not a text-to-video tool. It's a full production orchestration system where your AI coding assistant (Claude Code, Cursor, Copilot, Windsurf) becomes the director. Describe what you want in plain language. The agent researches, scripts, generates assets, edits, and renders the final video. Here's what the pipeline actually does: → Live web research first: 15-25+ searches across YouTube, Reddit, news sites before writing a single word of script → 12 video generation providers: Kling, Runway Gen-4, Google Veo 3, MiniMax, plus local GPU options (WAN 2.1, Hunyuan, CogVideo) → 8 image generation providers: FLUX, Google Imagen 4, DALL-E 3, Stable Diffusion locally → 4 TTS providers: ElevenLabs, Google (700+ voices), OpenAI, and Piper offline for free → WhisperX word-level subtitles burned in automatically → Remotion for React-based animated composition with spring physics, transitions, TikTok-style captions → Budget governance: cost estimate before execution, per-action approval above $0.50, hard cap at $10 Here's the wildest part: One product ad. 4 AI-generated images, TTS narration, royalty-free music, word-level subtitles, Remotion data visualizations. Total cost: $0.69. Zero manual asset work. Works with zero API keys too. Piper narrates locally, Pexels/Pixabay provide free stock, Remotion animates everything. No spend required to start. 100% Open Source. AGPL v3 License. (Link in the comments)
0:30

🚨BREAKING: An open-source agentic video production system just dropped. 11 pipelines, 49 tools, and a full product ad produced for $0.69 total. It's called OpenMontage. And it's not a text-to-video tool. It's a full production orchestration system where your AI coding assistant (Claude Code, Cursor, Copilot, Windsurf) becomes the director. Describe what you want in plain language. The agent researches, scripts, generates assets, edits, and renders the final video. Here's what the pipeline actually does: → Live web research first: 15-25+ searches across YouTube, Reddit, news sites before writing a single word of script → 12 video generation providers: Kling, Runway Gen-4, Google Veo 3, MiniMax, plus local GPU options (WAN 2.1, Hunyuan, CogVideo) → 8 image generation providers: FLUX, Google Imagen 4, DALL-E 3, Stable Diffusion locally → 4 TTS providers: ElevenLabs, Google (700+ voices), OpenAI, and Piper offline for free → WhisperX word-level subtitles burned in automatically → Remotion for React-based animated composition with spring physics, transitions, TikTok-style captions → Budget governance: cost estimate before execution, per-action approval above $0.50, hard cap at $10 Here's the wildest part: One product ad. 4 AI-generated images, TTS narration, royalty-free music, word-level subtitles, Remotion data visualizations. Total cost: $0.69. Zero manual asset work. Works with zero API keys too. Piper narrates locally, Pexels/Pixabay provide free stock, Remotion animates everything. No spend required to start. 100% Open Source. AGPL v3 License. (Link in the comments)

Guri Singh

112,201 görüntüleme • 3 ay önce

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.
2:38

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 görüntüleme • 7 ay önce

Andrej Karpathy said: "There's room for an incredible new product in the second brain space" This might be it. (bookmark it) Everyone is suddenly building a second brain. Karpathy's LLM wiki pattern went viral, and half of X is now hand-wiring Obsidian to Claude Code so an agent maintains their notes for them. The idea is beautiful: stop making your AI re-read raw notes on every question. Let it build a wiki that compounds. As Karpathy put it, "LLMs don't get bored, they don't forget to update a cross-reference (backlinks), and can touch 15 files in one pass." But if you start doing it manually, it becomes a project in itself. You wire up the vault, the agents, the schedules, the integrations, and then you babysit all of it. So I sat down with Arjun, who actually built the open source version of this, and we broke down what it looks like when the whole thing already works out of the box. It just crossed 15K stars on GitHub. Think Claude's desktop app, open source, with two things layered on top: → A work brain: background agents index your emails, meetings, and notes into a living knowledge graph that updates itself as you work. → Work surfaces: chat is not the best interface for real work, so you get an email client, a meeting note taker, a browser, and a code mode where you and the AI actually collaborate. The part that got me: a customer email comes in asking for a product change, a background agent triages it, spins up Claude Code in its own worktree, and the feature is written before you are back at your desk. Bring your existing Obsidian vault, connect Slack, X, and Fireflies, and let it run your day. Here's the full breakdown of what we covered in this session: Enjoy! 00:00 Intro 01:08 What is Roboat (an open source AI co-worker) 02:42 The second brain (a knowledge graph of your work) 04:01 Bringing your existing Obsidian vault in 04:46 Work surfaces 05:29 Meetings and automatic note taking 06:53 Connecting Slack, X and other sources 07:55 Background agents that run your day 09:24 Code mode (Claude Code and Codex) 10:18 Demo: from an email to written code 14:28 Guardrails: approvals and agent workspaces 17:15 Scheduling agents on a cron 18:52 The browser work surface (browser use) 20:42 Wrapping up: automating your whole day 22:44 Outro Checkout Rowboat's GitHub repo: (don't forget to star 🌟) My co-founder recently wrote a great article on the same idea, and I highly recommend reading it as well. The article is quoted below. Here's my session with Arjun:
22:53

Andrej Karpathy said: "There's room for an incredible new product in the second brain space" This might be it. (bookmark it) Everyone is suddenly building a second brain. Karpathy's LLM wiki pattern went viral, and half of X is now hand-wiring Obsidian to Claude Code so an agent maintains their notes for them. The idea is beautiful: stop making your AI re-read raw notes on every question. Let it build a wiki that compounds. As Karpathy put it, "LLMs don't get bored, they don't forget to update a cross-reference (backlinks), and can touch 15 files in one pass." But if you start doing it manually, it becomes a project in itself. You wire up the vault, the agents, the schedules, the integrations, and then you babysit all of it. So I sat down with Arjun, who actually built the open source version of this, and we broke down what it looks like when the whole thing already works out of the box. It just crossed 15K stars on GitHub. Think Claude's desktop app, open source, with two things layered on top: → A work brain: background agents index your emails, meetings, and notes into a living knowledge graph that updates itself as you work. → Work surfaces: chat is not the best interface for real work, so you get an email client, a meeting note taker, a browser, and a code mode where you and the AI actually collaborate. The part that got me: a customer email comes in asking for a product change, a background agent triages it, spins up Claude Code in its own worktree, and the feature is written before you are back at your desk. Bring your existing Obsidian vault, connect Slack, X, and Fireflies, and let it run your day. Here's the full breakdown of what we covered in this session: Enjoy! 00:00 Intro 01:08 What is Roboat (an open source AI co-worker) 02:42 The second brain (a knowledge graph of your work) 04:01 Bringing your existing Obsidian vault in 04:46 Work surfaces 05:29 Meetings and automatic note taking 06:53 Connecting Slack, X and other sources 07:55 Background agents that run your day 09:24 Code mode (Claude Code and Codex) 10:18 Demo: from an email to written code 14:28 Guardrails: approvals and agent workspaces 17:15 Scheduling agents on a cron 18:52 The browser work surface (browser use) 20:42 Wrapping up: automating your whole day 22:44 Outro Checkout Rowboat's GitHub repo: (don't forget to star 🌟) My co-founder recently wrote a great article on the same idea, and I highly recommend reading it as well. The article is quoted below. Here's my session with Arjun:

Akshay 🚀

45,815 görüntüleme • 22 gün önce

We made a thing! Very happy to announce sqlcoder-pro and the Defog Alignment Platform. Available to use immediately without a wait-list, weights will be open-sourced very soon. The video does a quick show and tell comparison against ChatGPT (with gpt-4o). Read on for more details! TLDR 💪 equal (or better) performance on text-to-SQL as the most capable Claude-3.5 or GPT-4 models 🤝 You can use it today on a free plan/free trial, without a waitlist 🪽 self-hostable on a single RTX4090, with 2 second median generation times for SQL queries 🔁 exactly the same output every time, give the same prompt 👨🏻‍🏫 teachable and steerable: show the model what you want it to do 🛞 debuggable – you can understand WTF is going on inside the model, instead of treating it like a black box Let's dig into each of these one-by-one! Performance SQLCoder-8b-pro significantly exceeds the performance of our previous sqlcoder-8b model on Postgres text-to-SQL (from 88.2% to 90.2% accuracy - gpt-4o is at 87.6%, for reference). It is also better at following instructions. This was done via self-merges, hand crafted fine-tuning data, and adapting the training data to fit our tokenizer. Cost You can host this on the model on a single $3,500 RTX4090, and support ~5 requests/second via VLLM. If you're looking to host on the cloud instead, you can run it on a single L4 GPU that costs $300/mo on GCP Repeatability We have a dense 8b model with no MoE shenanigans. For the same prompt with temperature=0, you'll always get the same answer – which is critical in BI. Teachable In our alignment and feedback modes, you can give the model feedback on how it answered certain questions, and it will automatically adapt to the feedback. Debuggable You can use logprobs and attention scores to determine where, exactly is the model paying attention to inside a prompt + what it's getting confused by when generating outputs. Available today You can use Defog on the cloud today by going to docs[dot]defog[dot]ai, and getting an API key. Excited to hear what you think!
2:33

We made a thing! Very happy to announce sqlcoder-pro and the Defog Alignment Platform. Available to use immediately without a wait-list, weights will be open-sourced very soon. The video does a quick show and tell comparison against ChatGPT (with gpt-4o). Read on for more details! TLDR 💪 equal (or better) performance on text-to-SQL as the most capable Claude-3.5 or GPT-4 models 🤝 You can use it today on a free plan/free trial, without a waitlist 🪽 self-hostable on a single RTX4090, with 2 second median generation times for SQL queries 🔁 exactly the same output every time, give the same prompt 👨🏻‍🏫 teachable and steerable: show the model what you want it to do 🛞 debuggable – you can understand WTF is going on inside the model, instead of treating it like a black box Let's dig into each of these one-by-one! Performance SQLCoder-8b-pro significantly exceeds the performance of our previous sqlcoder-8b model on Postgres text-to-SQL (from 88.2% to 90.2% accuracy - gpt-4o is at 87.6%, for reference). It is also better at following instructions. This was done via self-merges, hand crafted fine-tuning data, and adapting the training data to fit our tokenizer. Cost You can host this on the model on a single $3,500 RTX4090, and support ~5 requests/second via VLLM. If you're looking to host on the cloud instead, you can run it on a single L4 GPU that costs $300/mo on GCP Repeatability We have a dense 8b model with no MoE shenanigans. For the same prompt with temperature=0, you'll always get the same answer – which is critical in BI. Teachable In our alignment and feedback modes, you can give the model feedback on how it answered certain questions, and it will automatically adapt to the feedback. Debuggable You can use logprobs and attention scores to determine where, exactly is the model paying attention to inside a prompt + what it's getting confused by when generating outputs. Available today You can use Defog on the cloud today by going to docs[dot]defog[dot]ai, and getting an API key. Excited to hear what you think!

Rishabh Srivastava

13,460 görüntüleme • 1 yıl önce